Biostatistics Intern - SAS Programmer

Be The Match in; Minneapolis (MN); On-site; USD 30,000 - 47,000; Full time

Job summary

Be The Match is seeking a Biostatistics Intern - SAS Programmer (Finance) to support real-world evidence research within the Biostatistics team. The role focuses on data programming using SAS to prepare, transform, and quality-check datasets for observational studies and registry analyses.

You will collaborate with biostatisticians, data operations, and clinical teams to ensure clean, standardized, and analysis-ready data.

Qualifications

• Must be enrolled in the second year of a Master's degree program in Biostatistics, Statistics, Public Health, Data Science, or have graduated within the past 6 months.
• 1+ years of hands-on programming through research, internships, projects, or related work.
• Strong knowledge of Base SAS, PROC SQL, and data step programming.
• Familiarity with SAS macros.
• Availability to work 20-29 hours per week during standard business hours.
• Internship duration 9-12 months; anticipated start late August through mid-September 2026.

Responsibilities

• Support development of programming specifications defining variable derivations and data standards.
• Perform data cleaning, transformation, and preparation of large clinical registry datasets using SAS.
• Develop and maintain SAS programs and macros to support reproducible workflows.
• Translate specifications into SAS code to prepare analysis-ready datasets and implement derivation logic.
• Merge multiple data sources with QA assessments to ensure proper merging.
• Generate summary outputs such as tables, listings and frequency reports.

Skills

Base SAS

Data step programming

SAS macros

PROC SQL

Education

Master's degree in Biostatistics

Tools

SAS

SQL

Job description

Biostatistics Intern - SAS Programmer (Finance)

Job Description

POSITION SUMMARY:
The Biostatistics SAS Programmer Intern supports real-world evidence (RWE) research within the Biostatistics team. This role focuses on data programming via preparation, transformation, and quality control using SAS to support observational studies and registry-based analyses. The position collaborates closely with biostatisticians, data operations and clinical teams to ensure datasets are clean, standardized, and analysis-ready.

ACCOUNTABILITIES:

• Support development of programming specifications that define variable derivations and in-house data standards documentation.


• Perform data cleaning, transformation, and preparation of large clinical registry datasets using SAS.


• Develop and maintain SAS programs and macros to support efficient and reproducible workflows.


• Translate programming specifications provided by biostatisticians into SAS code to prepare analysis-ready datasets, including implementing derivation logic.


• Merge multiple data sources with high quality assurance assessments to ensure proper merging.


• Generate summary outputs such as tables, listings and frequency reports


• Document programming logic and dataset specifications. Utilize good programming practices that include comments and documentation.


• Implement data quality checks (QC).


• Troubleshoot data issues and collaborate with data operations team, if needed.

REQUIRED QUALIFICATIONS:

• Must be enrolled in the second year of a Master's degree program in Biostatistics, Statistics, Public Health, Data Science, or a related field, OR have graduated from such a Master's program within the past 6 months.


• 1+ years of hands-on programming experience through research assistantships, internships, academic projects, or other applied programming experiences.


• Strong knowledge of Base SAS, PROC SQL, and data step programming


• Familiarity with SAS macros


• Availability to work 20-29 hours per week during standard business hours.


• Expected internship duration is 9-12 months.


• Anticipated start date is late August through mid-September 2026.

PREFERRED QUALIFICATIONS:

• Knowledge of programming specifications


• Experience with healthcare, RWE, or registry data


• Exposure to QC automation or data pipelines


• Knowledge of R or Python is a plus